1. Field of the Invention
Assessing meat yield and quality in livestock carcasses is important for exporting meat and for the domestic consumer. It is important at all stages of the meat marketing chain from farm to retail sale. Since the meat grading system was first put into use in the United States in 1927, meat has been graded by human graders. The method used to grade meat is very subjective and, because of this subjective nature, it is very difficult (if not impossible) to achieve consistency and equity. The development of instruments to assist the human grader in evaluating grade factors has been attempted without much success.
This invention relates to a knowledge-based system for grading carcass meat.
2. Description of the Prior Art
In carcass beef grading, the carcasses are graded according to their degree or relative development of the desirable physical characteristics. The grade of the carcass is to reflect the differences in the portion of the desirable to less desirable parts of the carcass or cut, to reflect the ratio of meat to bone, and also to evaluate the characteristics of the meat which are associated with its ultimate palatibility. The USDA beef grading standards are designed to measure the quality (palatability) and the yield (cuttability) of the product as presented in carcass form. The quality grades are Prime, Choice, Select (formerly Good), Standard, Commercial, Utility, Cutter, and Canner, in order of decreasing palatability. The yield grades are Grades 1 through 5 , with Grade 1 having the highest yield of retail cut and Grade 5 the lowest yield of retail cut.
In present practice, meat graders reason about the final yield and quality grade of the carcasses using judgmental rules and empirical associations along with visual observation of the conditions of the carcasses. There are usually no algorithmic solutions except a formula for computing the preliminary yield grade of the carcass as given in the Meat Evaluation Handbook (National Live Stock and Meat Board, 1983). Since the grading of meat is accomplished by qualitative reasoning techniques rather than by mathematical or data processing procedures, this task represents an ideal application of a knowledge-based expert system.
Current developments in artificial intelligence programming, particularly in the expert systems field, make it possible to devise computer programs to assist humans in rendering decisions to complex judgemental type problems. As the name suggests, an expert system solves complex reasoning tasks that normally require an expert. A computer program in an artificial intelligence programming language deals with symbolic, nonalgorithmic methods of problem solving. These problem solving techniques relate symbols or objects through judgmental rules, or heuristics, as well as through theoretical laws and definitions. Expert systems programming uses artificial intelligence programming methods to solve problems conventional programming techniques cannot tackle.
A rule-based expert system, which is sometimes called an IF-THEN or situation-action system, consists of a knowledge-base and an inference engine. The knowledge-base contains information, consisting of facts and rules, about the specific area of interest and activity that the expert system deals with. Rules describe the logical relationships among elements of information within this area. The inference engine is the program that processes the rules and information and makes the inferences to arrive at specific conclusions.